Non-line-of-sight correction for target detection and identification in point clouds

ABSTRACT

Examples disclosed herein relate to an autonomous driving system in a vehicle having a radar system with a Non-Line-of-Sight (“NLOS”) correction module to correct for NLOS reflections prior to the radar system identifying targets in a path and a surrounding environment of the vehicle, and a sensor fusion module to receive information from the radar system on the identified targets and compare the information received from the radar system to information received from at least one sensor in the vehicle.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Non-Provisional applicationSer. No. 16/403,458, filed on May 3, 2019, and incorporated herein byreference in their entirety, which claims priority to U.S. ProvisionalApplication No. 62/666,666, filed on May 3, 2018, and incorporatedherein by reference in their entirety.

BACKGROUND

Autonomous driving is quickly moving from the realm of science fictionto becoming an achievable reality. Already in the market areAdvanced-Driver Assistance Systems (“ADAS”) that automate, adapt andenhance vehicles for safety and better driving. The next step will bevehicles that increasingly assume control of driving functions such assteering, accelerating, braking and monitoring the surroundingenvironment and driving conditions to respond to events, such aschanging lanes or speed when needed to avoid traffic, crossingpedestrians, animals, and so on. The requirements for object and imagedetection are critical and specify the time required to capture data,process it and turn it into action. All this while ensuring accuracy,consistency and cost optimization.

An aspect of making this work is the ability to detect and classifyobjects in the surrounding environment at the same or possibly evenbetter level as humans. Humans are adept at recognizing and perceivingthe world around them with an extremely complex human visual system thatessentially has two main functional parts: the eye and the brain. Inautonomous driving technologies, the eye may include a combination ofmultiple sensors, such as camera, radar, and lidar, while the brain mayinvolve multiple artificial intelligence, machine learning and deeplearning systems. The goal is to have full understanding of a dynamic,fast-moving environment in real time and human-like intelligence to actin response to changes in the environment.

BRIEF DESCRIPTION OF THE DRAWINGS

The present application may be more fully appreciated in connection withthe following detailed description taken in conjunction with theaccompanying drawings, which are not drawn to scale and in which likereference characters refer to like parts throughout, and wherein:

FIG. 1 is a schematic diagram showing a radar being used to identifytargets in its surrounding environment;

FIG. 2 is a schematic diagram of a radar in an autonomous driving systemin accordance with various examples;

FIG. 3 is a schematic diagram of an autonomous driving system inaccordance with various examples;

FIG. 4 illustrates an example environment in which an autonomous vehiclewith the system of FIGS. 2 and 3 is used to detect and identify objects;

FIG. 5 is a schematic diagram of a radar system with a NLOS correctionmodule for use in the autonomous driving system of FIG. 3 in accordancewith various examples;

FIG. 6 is a schematic diagram illustrating the two main stages of a NLOScorrection module for use in a radar in an autonomous driving system;and

FIG. 7 is a flowchart illustrating the operation of the NLOS correctionmodule of FIG. 6 .

DETAILED DESCRIPTION

Systems and methods for Non-Line-of-Sight (“NLOS”) correction for targetdetection in point clouds are disclosed. The systems and methods aresuitable for many different applications and can be deployed in avariety of different environments and configurations. In variousexamples, the systems and methods are used in a radar system in anautonomous driving vehicle to identify and classify objects in thesurrounding environment in all-weather conditions and beyondline-of-sight. The targets may include structural elements in theenvironment such as roads, walls, buildings, road center medians andother objects, as well as vehicles, pedestrians, bystanders, cyclists,animals and so on. The point cloud is generated by an imaging sensor ordetecting device, such as a camera, a radar, a lidar or other device,and contain data representing targets captured by the detecting device.

As described in more detail below, the radar system in various exampleshas a meta-structure (“MTS”) antenna capable of steering beams withcontrollable parameters in any desired direction in a 360° field ofview. The radar system has a Perception Module that provides control ofthe MTS antenna in response to a detected and identified target in thesurrounding environment. This enables the radar system to provide adynamically controllable and steerable antenna beam that can focus onone or multiple portions within a 360° field of view, while optimizingthe antenna capabilities and reducing the processing time andcomputational complexity for identification of objects therein.

It is appreciated that, in the following description, numerous specificdetails are set forth to provide a thorough understanding of theexamples. However, it is appreciated that the examples may be practicedwithout limitation to these specific details. In other instances,well-known methods and structures may not be described in detail toavoid unnecessarily obscuring the description of the examples. Also, theexamples may be used in combination with each other.

Referring now to FIG. 1 , a schematic diagram of a radar being used toidentify targets in its surrounding environment is described. MTS radarsystem 102 transmits RF waves with a MTS antenna that get reflected offof targets and other objects in environment 100. In various examples,MTS radar 102 may be part of an autonomous driving system in a vehicleto identify targets in the vehicle's path and in its surroundingenvironment. An actual target 104 is positioned as shown and reflectsthe RF waves back to the MTS radar 102. Due to multi-path propagation ofthe reflected waves, MTS radar 102 may perceive an NLOS reflection 106of the actual target 104 about a planar reflecting surface 108. The MTSradar 102 may perceive the NLOS reflection 106 as an actual targetrather than a reflection of the actual target 104, thereby causing theautonomous driving system to incorrectly detect a target. As describedbelow, the systems and methods disclosed herein enable the autonomousdriving system to identify the planar reflecting surface 108 as a planarsurface and correctly localize it relative to the actual target 104 andits NLOS reflection 106.

Attention is now directed to FIG. 2 , which shows a schematic diagram ofa radar in an autonomous driving system in accordance with variousexamples. Autonomous driving system 200 includes an intelligent radarsystem 202 and other sensor systems 204 such as camera, lidar sensors,environmental sensors, operational sensors, and so on. Intelligent radarsystem 202 has MTS Radar Module 206 to gather a point cloud S of datarepresenting targets in the path of a vehicle with autonomous drivingsystem 200 and in its surrounding environment. Each point in the cloudhas a location (x, y, z) and a brightness B, as well as velocityinformation. The values of {right arrow over (r)}=(x, y, z) aredetermined by radar processing (i.e., by acquiring range and azimuth andelevation information at the time of capture) and simply transformingthese values into cartesian coordinates.

As discussed above with reference to FIG. 1 , for some elements of S,target identification may be confounded by NLOS reflections of targetsabout a planar reflecting surface. A NLOS correction module 208 isintroduced as a pre-processing step in the intelligent radar system 202to generate a corrected point cloud that is fed into Perception Module210 for accurate identification of targets. In various examples, othersensor systems 204 (e.g., cameras, lidar sensors, etc.) may be used toprovide a supplemental point cloud to NLOS correction module 208 forenhanced correction of NLOS reflections.

FIG. 3 is a schematic diagram of an autonomous driving system inaccordance with various examples. Autonomous driving system 300 is asystem for use in a vehicle that provides some or full automation ofdriving functions. The driving functions may include, for example,steering, accelerating, braking and monitoring the surroundingenvironment and driving conditions to respond to events, such aschanging lanes or speed when needed to avoid traffic, crossingpedestrians, animals, and so on. Autonomous driving system 300 includesa radar system with a NLOS correction module 302, sensor systems 304,system controller 306, system memory 308, communication bus 310 andsensor fusion 312. It is appreciated that this configuration ofautonomous driving system 300 is an example and is not meant to belimiting to the specific structure illustrated in FIG. 3 . Additionalsystems and modules not shown in FIG. 3 may be included in autonomousdriving system 300.

Radar system with NLOS correction module 302 includes an MTS antenna forproviding dynamically controllable and steerable beams that can focus onone or multiple portions within a 360° field of view. The beams radiatedfrom the MTS antenna are reflected back from targets in the vehicle'ssurrounding environment and received and processed by the radar system302 to detect and identify the targets. As generally used herein, thetargets may include structural elements in the environment such aswalls, buildings, road center medians, and other objects, as well asvehicles, pedestrians, bystanders, cyclists, animals and so on. Theradar system 302 has a reinforcement learning engine that is trained todetect and identify the targets and control the MTS antenna module asdesired.

Sensor systems 304 may include multiple sensors in the vehicle, such ascameras, lidar, ultrasound, communication sensors, infrastructuresensors and other sensors to determine a condition of the surroundingenvironment and in particular, to comprehend the path of the vehicle soas to anticipate, identify and detect the presence of a target in thevehicle's path. Data from radar system 302 and sensor systems 304 may becombined in sensor fusion module 312 to improve the target detection andidentification performance of autonomous driving system 300. Sensorfusion module 312 is controlled by system controller 306, which may alsointeract with and control other modules and systems in the vehicle. Forexample, system controller 306 may turn the different sensors in sensorsystems 304 on and off as desired, or provide instructions to thevehicle to stop upon identifying a driving hazard (e.g., deer,pedestrian, cyclist, or another vehicle suddenly appearing in thevehicle's path, flying debris, etc.)

All modules and systems in autonomous driving system 300 communicatewith each other through communication bus 310. Autonomous driving system300 also includes system memory 308, which may store information anddata (e.g., static and dynamic data) used for operation of system 300and the vehicle using system 300.

FIG. 4 illustrates an example environment in which an autonomous vehiclewith the systems of FIGS. 2 and 3 is used to detect and identifyobjects. Vehicle 400 is an autonomous vehicle with an MTS radar system406 for transmitting a radar signal to scan a FoV or specific area. Invarious examples, the radar signal is transmitted according to a set ofscan parameters that can be adjusted to result in multiple transmissionbeams 418. The scan parameters may include, among others, the totalangle of the scanned area from the radar transmission point, the powerof the transmitted radar signal, the scan angle of each incrementaltransmission beam, as well as the angle between each beam or overlaptherebetween. The entire FoV or a portion of it can be scanned by acompilation of such transmission beams 418, which may be in successiveadjacent scan positions or in a specific or random order. Note that theterm FoV is used herein in reference to the radar transmissions and doesnot imply an optical FoV with unobstructed views. The scan parametersmay also indicate the time interval between these incrementaltransmission beams, as well as start and stop angle positions for a fullor partial scan.

In various examples, the vehicle 400 may also have other perceptionsensors, such as camera 402 and lidar 404. These perception sensors arenot required for the vehicle 400, but may be useful in augmenting theobject detection capabilities of the radar system 406, which has a NLOScorrection module for enhanced correction of NLOS reflections and areinforcement learning engine that is trained to detect and identifytargets in the path and surrounding path of the vehicle, such asvehicles 410 and 414, which in this illustrated example are autonomousvehicles equipped with lidars 412 and 416, respectively.

In various examples and as described in more detail below, the MTS radarsystem 406 is capable of providing a 360° true 3D vision and human-likeinterpretation of the vehicle's path and surrounding environment. Theradar system 406 is capable of shaping and steering RF beams in alldirections in a 360° FoV with at least one beam steering antenna. Thisenables the radar system 406 to recognize objects quickly and with ahigh degree of accuracy over a long range of around 300 meters or more.The short range capabilities of camera 402 and lidar 404 along with thelong range capabilities of radar 406 enable a sensor fusion module 408in vehicle 400 to advance the possibility of fully self-driving cars.The object detection and identification performance provided by thereinforcement learning engine in radar system 406 can be used to reduceor minimize the scan performance of the radar system 406, as the engineenables objects to be detected and identified with less stringent scanparameters for the radar 406 as it would otherwise be needed. Further,the use of the NLOS correction module enables an enhanced correction ofNLOS reflections that further improves the object detection andidentification performance of the radar system 406.

Attention is now directed to FIG. 5 , which illustrates a schematicdiagram of a radar system with a NLOS correction module for use in theautonomous driving system of FIG. 3 in accordance with various examples.Radar system 500 is a “digital eye” with true 3D vision and capable of ahuman-like interpretation of the world. The “digital eye” and human-likeinterpretation capabilities are provided by two main modules: MTS RadarModule 502 and Perception Module 504.

MTS radar module 502 includes at least one beam steering antenna 506 forproviding dynamically controllable and steerable beams that can focus onone or multiple portions of a 360° FoV of a vehicle. In variousexamples, the beam steering antenna is an MTS antenna capable ofradiating RF signals in millimeter wave frequencies. A meta-structure,as generally defined herein, is an engineered, non- or semi-periodicstructure that is spatially distributed to meet a specific phase andfrequency distribution. The meta-structure antenna may be integratedwith various structures and layers, including, for example, feed networkor power division layer 510 to divide power and provide impedancematching, RFIC 508 to provide steering angle control and otherfunctions, and a meta-structure antenna layer with multiple microstrips,gaps, patches, vias, and so forth. The meta-structure layer may include,for example, a metamaterial layer. Various configurations, shapes,designs and dimensions of the beam steering antenna 506 may be used toimplement specific designs and meet specific constraints.

Radar control is provided in part by the perception module 504. Radardata generated by the radar module 502 is provided to the perceptionmodule 504 for object detection and identification. The radar data isacquired by the transceiver 512, which has a radar chipset capable oftransmitting the RF signals radiated by the beam steering antenna 506and receiving the reflections of these RF signals. The transceivermodule 512 prepares a signal for transmission, such as a signal for aradar device, wherein the signal is defined by modulation and frequency.The signal is provided to the beam steering antenna 506 through acoaxial cable or other connector and propagates through the structurefor transmission through the air via RF beams at a given phase,direction, and so on. The RF beams and their parameters (e.g., beamwidth, phase, azimuth and elevation angles, etc.) are controlled byantenna controller 514, such as at the direction of perception module504.

The RF beams reflect off of targets in the surrounding environment andthe RF reflections are received by the transceiver module 512. Radardata from the received RF beams is provided to the perception module 504for target detection and identification. The radar data may be organizedin sets of Range-Doppler (“RD”) map information, corresponding to 4Dinformation that is determined by each RF beam radiated off targets,such as azimuthal angles, elevation angles, range and velocity. The RDmaps are extracted from frequency-modulated continuous wave (“FMCW”)radar pulses and they contain both noise and systematic artifacts fromFourier analysis of the pulses. The perception module 504 controlsfurther operation of the radar module 502 by, for example, providingbeam parameters for the next RF beams to be radiated from MTS radiatingcells in the beam steering antenna 506.

In various examples, the transmission signals sent by transceiver module512 are received by a portion, or subarray, of beam steering antenna506, which is an array of individual MTS radiating cells (e.g., an 8×16array), wherein each of the cells has a uniform size and shape. In someexamples, the MTS radiating cells may incorporate different sizes,shapes, configurations and array sizes. The MTS radiating cells includea variety of conductive structures and patterns, such that a receivedtransmission signal is radiated therefrom. The MTS array in antenna 506is a periodic arrangement of MTS cells that are each smaller than thetransmission wavelength.

In some examples, the MTS cells may be metamaterial (“MTM”) cells. EachMTM cell has some unique properties. These properties may include anegative permittivity and permeability resulting in a negativerefractive index; these structures are commonly referred to asleft-handed materials (“LHM”). The use of LHM enables behavior notachieved in classical structures and materials, including interestingeffects that may be observed in the propagation of electromagneticwaves, or transmission signals. Metamaterials can be used for severalinteresting devices in microwave and terahertz engineering such asantennas, sensors, matching networks, and reflectors, such as intelecommunications, automotive and vehicular, robotic, biomedical,satellite and other applications. For antennas, metamaterials may bebuilt at scales much smaller than the wavelengths of transmissionsignals radiated by the metamaterial. Metamaterial properties come fromthe engineered and designed structures rather than from the basematerial forming the structures. Precise shape, dimensions, geometry,size, orientation, arrangement and so forth result in the smartproperties capable of manipulating EM waves by blocking, absorbing,enhancing, or bending waves.

In operation, the antenna controller 514 is responsible for directingthe beam steering antenna 506 to generate RF beams with determinedparameters such as beam width, transmit angle, and so on. The antennacontroller 514 may, for example, determine the parameters at thedirection of the perception module 504, which may at any given time wantto focus on a specific area of a field of view upon identifying targetsof interest in the vehicle's path. The antenna controller 514 determinesthe direction, power, and other parameters of the beams and controls thebeam steering antenna 506 to achieve beam steering in variousdirections. The antenna controller 514 also determines a voltage matrixto apply to RFIC 508 to achieve a given phase shift. The radarperception module 404 provides control actions to the antenna controller514 at the direction of Reinforcement Learning Engine 516.

Next, the antenna 506 radiates RF beams having the determinedparameters. The RF beams are reflected off of targets in and around thevehicle's path (e.g., in a 360° field of view) and are received by thetransceiver module 512 in the MTS radar module 502. The MTS radar module502 then transmits a point cloud containing 4D radar data to the NLOScorrection module 524 for generating a corrected point cloud that isthen sent to the perception module 504. A micro-doppler module 522coupled to the MTS radar module 502 and the perception module 504extracts micro-doppler signals from the 4D radar data to aid in theidentification of targets by the perception module 504. Themicro-doppler module 522 takes a series of RD maps from the MTS radarmodule 502 and extracts a micro-doppler signal from them. Themicro-doppler signal enables a more accurate identification of targetsas it provides information on the occupancy of a target in variousdirections.

The reinforcement learning engine 516 receives the corrected point cloudfrom the NLOS correction module 524, processes the radar data to detectand identify targets, and determines the control actions to be performedby the MTS radar module 502 based on the detection and identification ofsuch targets. For example, the reinforcement learning engine 516 maydetect a cyclist on the path of the vehicle and direct the MTS radarmodule 502, at the instruction of its antenna controller 514, to focusadditional RF beams at given phase shift and direction within theportion of the field of view corresponding to the cyclist's location.

The perception module 504 also includes a multi-object tracker 518 totrack the identified targets over time, such as, for example, with theuse of a Kalman filter. Information on identified targets over time arestored at an object list and occupancy map 520, which keeps tracks oftargets' locations and their movement over time as determined by themulti-object tracker 518. The tracking information provided by themulti-object tracker 518 and the micro-doppler signal provided by themicro-doppler module 522 are combined to produce an output containingthe type of target identified, their location, their velocity, and soon. This information from radar system 500 is then sent to a sensorfusion module such as sensor fusion module 312 of FIG. 3 , where it isprocessed together with object detection and identification from othersensors in the vehicle.

FIG. 6 illustrates the two main stages of a NLOS correction module foruse in a radar in an autonomous driving system, e.g., NLOS correctionmodule 524 in radar 500 of FIG. 5 . NLOS correction module 600,implemented as in NLOS correction module 208 of FIG. 2 , NLOS correctionmodule in radar system 302 of FIG. 3 or NLOS correction module 524 inradar system 500 of FIG. 5 , receives a radar point cloud 602 andgenerates a corrected point cloud 604 to properly account for NLOSreflections of actual LOS targets and provide an accurate localizationof NLOS targets. NLOS correction module 600 has in essence two tasks toperform: for all points s_(i)∈S, (1) is s_(i) the result of a reflectionfrom a planar reflecting surface? (2) If so, where is the true locationof the target corresponding to s_(i)?

The first task is performed by Planar Surface Identification Module 606,which locates all significant planar reflecting surfaces in the field ofview of the radar system incorporating NLOS correction module 600. Oncethe plane reflecting surfaces are located, the second task is performedby NLOS Reflection Remapping Module 608, which remaps the NLOSreflections of a target about the identified planar reflecting surfacesto determine a best estimate of its true location.

Note that the planar surface identification module 606 may also receivea supplemental point cloud 610, e.g., a lidar point cloud, to aid in theidentification of the planar reflecting surfaces. The planar surfaceidentification module 606 may, for example, identify the planarreflecting surfaces in the supplemental point cloud 610 and then remapthe NLOS reflections in NLOS reflection remapping module 608 in theradar point cloud 602. Alternatively, the identification of the planarreflecting surfaces may be performed with the radar point cloud 602using the supplemental point cloud 610 to verify that the planarreflecting surfaces were located correctly. The vice-versa scenario mayalso be used, with the supplemental point cloud 610 providing the datafor the identification and the radar point cloud 602 providing the datato confirm that the identification is correct. Further, theidentification may be performed in both of point clouds 602 and 610 andthe results may be compared to determine the planar reflecting surfacelocations. It is appreciated that a number of point clouds may be usedin this identification of planar reflecting surfaces by planar surfaceidentification module 606. The NLOS reflection remapping module 608remaps the NLOS reflections about the identified planar reflectingsurfaces using the radar point cloud 602.

Attention is now directed to FIG. 7 , which illustrates the operation ofthe NLOS correction module of FIG. 6 in more detail. NLOS correctionmodule 700 starts out by applying the planar identification module 706to a point cloud S (704). The point cloud may be a radar point cloudsuch as radar point cloud 702 or a supplemental point cloud.Alternatively, both point clouds may be used to generate two resultsthat are compared. In various examples, planar surface identificationmodule 606 implements a 3D Kernel-Based Hough Transform (“3DKHT”) todetect the planar reflecting surfaces from the point cloud. The resultof the 3DKHT application to a point cloud S is a list of L candidateplanar surfaces with corresponding locations, orientations, andconfidence estimates.

Candidate planar surfaces are compared to a confidence brightnessthreshold to indicate when there truly is a significant planar surfacein the field of view. The spurious surfaces, i.e., candidate surfacesthat are below the confidence brightness threshold, are then discarded(706). In general, the cost for false negative results (failing topredict a planar reflecting surface when in fact one exists) is muchlower than the cost for false positives (predicting a reflection wherenone exists). Due to the high cost of false positives, it is likely thatthe confidence brightness threshold may be set high.

With the planar reflecting surfaces now identified, the point cloud S istransformed into a spherical coordinate system centered on the radaritself (708). The angular space of the point cloud S, i.e., the azimuthand elevation angles (ϕ,θ), is discretized into k² bins (710). For eachof the L planar surfaces, NLOS correction module 600 proceeds to extractthe bins that the planar surface intersects (712). The planar surface'sposition and its surface normal vector are also extracted (714). If twoplanar surfaces intersect the same bin, the more distance surface isignored. For discussion and illustration purposes, consider that the Lplanar surfaces intersect M bins. The surface positions of theidentified L planar surfaces in each bin intersection and their surfacenormal vector define M different reflection operations about therelevant surfaces (716). For each affected bin, the coordinates of thepoints in S whose distance from the radar exceeds the distance from theradar to the intersecting plane are then remapped by a reflection aboutthe intersecting plane to locate the targets (718).

Note that this reflection operation can be defined in O(l) for each binand performed in O(n) where n is the number of points to be reflected.Since each bin is expected to have on average N/k² points, and MαLk²,the entire reflection operation is expected to scale as LMN/k²≅LN. Ifthe confidence brightness threshold is kept high, there will not be anenormous number of planar surfaces, and so this scaling will be fine.Note also that the 3DKHT implementation for the planar surfaceidentification module 706 is a deterministic method of planar Houghtransformation which runs in N log N. The 3DKHT implementation has lowenough computational and memory cost to be feasible on inexpensivehardware in real time. It is appreciated that other implementations foridentifying planar reflecting surfaces may also be used by PlanarSurface Identification Module 306.

It is also appreciated that there may a fair amount of trial and errorin determining the proper confidence brightness threshold. One approachis to simplify the planar identification by looking first for horizontalplanes. Further accuracy can be obtained by filtering out points due totargets with a non-zero velocity relative to a road, since theydefinitely do not correspond to a fixed planar surface. Suchimplementation may be used for example to image the back of a vehicletwo places ahead of the autonomous driving vehicle in a line of cars, orimage vehicles moving behind a line of stopped cars.

The various examples described herein support autonomous driving withimproved sensor performance, all-weather/all-condition detection,advanced decision-making algorithms and interaction with other sensorsthrough sensor fusion. These configurations optimize the use of radarsensors, as radar is not inhibited by weather conditions in manyapplications, such as for self-driving cars. The ability to captureenvironmental information early aids control of a vehicle, allowinganticipation of hazards and changing conditions. Sensor performance isalso enhanced with these structures, enabling long-range and short-rangevisibility. In an automotive application, short-range is consideredwithin 30 meters of a vehicle, such as to detect a person in a crosswalk directly in front of the vehicle; and long-range is considered to250 meters or more, such as to detect approaching cars on a highway.These examples provide automotive radars capable of reconstructing theworld around them and are effectively a radar “digital eye,” having true3D vision and capable of human-like interpretation of the world.

It is appreciated that the previous description of the disclosedexamples is provided to enable any person skilled in the art to make oruse the present disclosure. Various modifications to these examples willbe readily apparent to those skilled in the art, and the genericprinciples defined herein may be applied to other examples withoutdeparting from the spirit or scope of the disclosure. Thus, the presentdisclosure is not intended to be limited to the examples shown hereinbut is to be accorded the widest scope consistent with the principlesand novel features disclosed herein.

What is claimed is:
 1. A sensor system, comprising: a sensor moduleadapted to: detect objects in a field of view of the sensor module;detect reflections from a reflective surface outside the field of view;and correct for the reflective surface to detect objects in the field ofview.
 2. The sensor system as in claim 1, wherein the sensor scans thefield of view.
 3. The sensor system as in claim 2, wherein sensor moduleis a radar system having an array of radiating structures.
 4. The sensorsystem as in claim 3, wherein the sensor system is further configured toreceive a point cloud from the radar system and generate a correctedpoint cloud.
 5. The autonomous driving system of claim 1, wherein theNLOS correction module receives a supplemental point cloud from the atleast one sensor in the vehicle.
 6. The sensor system as in claim 1,wherein the reflective surface is a planar surface.
 7. The sensor systemof claim 1, wherein the sensor module is further adapted to identify atleast one planar reflecting surface using a supplemental point cloud. 8.The sensor system as in claim 1, wherein the sensor module is furtheradapted to generate a point cloud of data representing sensordetections.
 9. The sensor system as in claim 1, wherein the sensormodule is further adapted to generate corrected point cloud of datausing detected relfections.
 10. The sensor system as in claim 9, whereinthe sensor module is further adapted to map detected reflections. 11.The sensor system as in claim 1, further comprising a perception moduleto determine a control action for the sensor module.
 12. A method foroperating a sensor system, the method comprising: directing an antennato generate RF beams at a plurality of directions in a field of view;receiving reflected RF beams from targets in the field of view;generating a point cloud from the reflected RF beams; detecting objectsfrom outside of the field of view from a reflective surface in the fieldof view; and identifying at least one target from the corrected pointcloud.
 13. The method as in claim 12, further comprising: locating thereflective surface in the field of view.
 14. The method as in claim 13,further comprising applying a threshold to filter out spurious surfaces.15. The method as in claim 14, further comprising transforming the pointcloud into spherical coordinate system.
 16. The method as in claim 15,further comprising discretizing angular space of the point cloud into apredetermined number of bins.
 17. The method as in claim 16, furthercomprising extracting intersecting bins for each planar surface.
 18. Themethod of claim 17, further comprising extracting position and surfacenormal vector information for each planar surface.
 19. The method as inclaim 18, further comprising define reflection operations defined byextracted position and the surface normal vector information.
 20. Themethod as in claim 19, further comprising remapping reflections todetermine locations of targets.